1. Field of Invention
The present invention relates to a system for creating parameter information that creates parameter information used in simulations of electronic circuits, a system for estimating yields that estimates yields of the electronic circuits, and to a computer program (hereafter, a program) and recording medium.
2. Background Art
Heretofore, the system for creating parameter information for use in simulation of the electronic circuits, and the system for estimating yields of the electronic circuits have been developed. Conventionally, the following three stage processes are performed in a system for estimating yields that adopted a SPICE (Simulation Program with Integrated Circuit Emphasis) which is a kind of simulation systems; (1) a stage 1 where a model library is derived for the SPICE, (2) a stage 2 where a typical SPICE model library and standard deviations for each of the SPICE model parameters are derived, (3) a stage 3 where a yield for a characteristic in question (an objective characteristic) is derived (for example, U.S. Pat. No. 6,978,229, and Sang-Hoon Lee et al., “An Efficient Statistical Analysis Methodology and Its Application to High-Density DRAMs,” IEEE/ACM Proceedings of ICCAD'97, 1997, pp. 678-683.)
FIG. 11 shows a process flow in stage 1 in the conventional system for estimating yields. The purpose of stage 1 is to extract the SPICE model for devices used in an electronic circuit for the objective characteristics. In FIG. 11, after a TEG (Test Element Group; devices for characterization to find out problems due to design or manufacturing process) for creating the SPICE model library has been fabricated (Step S1101), electronic characteristics of the TEG are measured (step S1102.) The SPICE model library is created using the electric characteristics 1102 obtained. The SPICE model library is obtained by this step. In order to evaluate variations by devices, stage 1 is repeated, which brings multiple SPICE model libraries 1103.
FIG. 12 shows a process flow of stage 2. The purpose of this stage is to create a typical SPICE model library from the multiple SPICE model libraries 1103 obtained as described above and to extract standard deviations of each model parameter for the SPICE model library. For the typical SPICE model library, median value or average value and so on for the multiple SPICE model libraries depending on a purpose.
In FIG. 12, the typical SPICE model library is created from the multiple SPICE model libraries 1103 obtained as described above, and standard deviations for each of model parameters for the SPICE model library are extracted (step S1201.) With this step, the typical SPICE model library 1202, standard deviations 1203 for each of the SPICE model parameters, and a table for correlation coefficients 1204 indicating a correlation among the SPICE model parameters, are obtained.
FIG. 13 shows a process flow for stage 3. The purpose of this stage is to derive a yield of a characteristic (the objective characteristic) in question of yield. In FIG. 13, typical SPICE model library 1202, a standard deviation 1203 for each of the SPICE model parameters, the table for correlation coefficients 1204 between the SPICE model parameters, SPICE net list 1304 for the objective characteristic corresponding to the objective characteristic, plural standard normal random number sequences 1305 generating random numbers corresponding to the correlation coefficients between the SPICE model parameters are used to generate the SPICE model library having correlated model parameters with variations, and the SPICE net list for the objective characteristic for N times SPICE simulations collectively (step S1301). With this step, the SPICE model library having model parameters with variations and the SPICE net list 1301 for the objective characteristic are obtained for N times of simulations collectively.
Next, N times of simulations are performed using the SPICE model library having model parameters with variations and the SPICE net list 1301 for the objective characteristic for N times of simulations (step S1302.) With this step, the objective characteristic values with variations 1302 are obtained corresponding to N times of simulations. Then, for the objective characteristics values with variations, a frequency of satisfying a given specification (Pass) and a frequency of not satisfying the specification (Fail) are examined, and the yield Y (=Pass/N) is estimated. Thus, the yield is obtained.
A method to make stage 1 and 2 more efficient has been reported in Takeuchi, Hane “High efficiency Extraction Method of Statistical SPICE Parameter,” The Japan Society of Applied Physics, Silicon Technology, No. 76, 2005, pp 41-45. A method to make stage 3 more efficient has been reported in Singhee et al., “Recursive Statistical Blockade: An Enhanced Technique for Rare Event Simulation with Application to SRAM Circuit Design,” IEEE 21st International Conference on VLSI Design, 2008, pp. 131-136. In the method shown in Takeuchi, Hane, “High efficiency Extraction Method of Statistical SPICE Parameter,” The Japan Society of Applied Physics, Silicon Technology, No. 76, 2005, pp 41-45 for stage 1 and 2, the library creation must be repeated as much as the measuring sample numbers, which causes a problem that the creation of the SPICE model library is time consuming.
In the method shown in Takeuchi, Hane “High efficiency Extraction Method of Statistical SPICE Parameter,” The Japan Society of Applied Physics, Silicon Technology, No. 76, 2005, pp 41-45, the standard deviations are guessed by random guess or based on experiences. The problems of this method are that there is a possibility of taking a long time to find a correct standard of deviations and that even if found, it takes time to derive the standard deviations and the correlation coefficients. In the method disclosed in U.S. Pat. No. 6,978,229 or Sang-Hoon Lee et al., “An Efficient Statistical Analysis Methodology and Its Application to High-Density DRAMs,” IEEE/ACM Proceedings of ICCAD'97, 1997, pp. 678-683, Monte Carlo repetition frequency over the inverse of a failure rate is required. This makes the SPICE repetition frequency enormous when the failure rate is small and causes a problem in that analysis does not complete within a practical time.
In the method of stage 3 disclosed in Singhee et al., “Recursive Statistical Blockade An Enhanced Technique for Rare Event Simulation with Application to SRAM Circuit Design,” IEEE 21st International Conference on VLSI Design, 2008, pp. 131-136, speeding up may hardly be possible when the number of the variation model parameters increases. Also in stage 3 under the conventional method, the frequency for the Monte Carlo repetition and the number of the model parameters have to be lesser than required in order to complete the plural SPICE simulations within a practical time. This causes a problem in that accuracy of estimating the yield lowers.
On the other hand, Importance Sampling method is known as an accelerating method, as disclosed in R. Kanji, R. Joshi, S. Nassif, “Mixture Importance Sampling and its Application to the Analysis of SRAM Designs in the Presence of Rare Failure Events,” Proceedings of Design Automation Conference 2006, pp. 69-72, July 2006. The importance sampling method takes samples with larger weight from a specific area expected to be important, instead of taking samples from all the sampling area in accordance with intrinsic natural probability distribution. For example, in estimating an occurrence probability of a very rare phenomenon, samples are taken following a probability distribution with much frequent occurrences and then a conversion to the actual occurrence probability is made when estimation is made.
A problem to be solved by the present invention is to create parameter information with high speed and with high accuracy for the use in the simulation of the electronic circuits. Another problem to be solved by the present invention is to estimate yields with high speed and high accuracy for objective characteristics taking into account variations in devices of the electronic circuits.